One of the oldest citizen science projects is bird watching. For more than a century, enthusiastic birders have amassed vast datasets of avian sightings. To date, technology has enhanced but not displaced this proud nerd army. Photography, GPS, and databases have vastly improved the data from birders, but nothing has replaced boots on the ground.
This month, a research project at the University of Adelaide reported a demonstration of a UAV mounted image system that, for once, beats human birders .
Specifically, the study compared the accuracy of humans versus a small survey quadcopter, on a task to count birds in a nesting colony. In order to have a known ground truth, the tests used artificial colonies, populated by hundreds of simulated birds. The repurposed decoys were laid out to mimic some actual nesting sites.
They dubbed it “#EpicDuckChallenge”, though it doesn’t seem especially “epic” to me.
The paper compares the accuracy of human counters on the ground, human counts from the aerial imagery, and computer analysis of the aerial imagery.
First of all, the results show a pretty high error for the human observers, even for the experienced ecologists in the study. Worse, the error is pretty scattered, which suggests that estimates of population change over time will be unreliable.
The study found that using aerial photos from the UAV is much, much more accurate than humans on the ground. The UAV imagery has the advantage of being overhead (rather than human eye level), and also holds still for analysis.
However, counting birds in an image is still tedious and error prone. The study shows that machine learning can tie or beat humans counting from the same images.
Together, the combination of low-cost aerial images and effective image processing algorithms gave very accurate results, with low variability. This means that this technique would be ideal for monitoring populations over time, because repeated flyovers would be reliably counted.
This study has its limitations, of course.
For one thing, the specific task used is pretty much the best possible case for such an aerial census. Unrealistically ideal, if you ask me.
Aside from the perfect observing conditions, the colony is easily visible (on an open, flat, uniform surface), and the ‘birds’ are completely static. In addition, the population is uniform (only one species), and the targets are not camouflaged in any way.
How many real-world situations are this favorable? (Imagine using a UAV in a forest, at night, or along a craggy cliff.)
To the degree that the situation is less than perfect, the results will suffer. In many cases, the imagery will be poorer, and the objects to be counted less distinct and recognizable. Also, if there are multiple species, very active birds, or visual clutter such as shrubs, it will be harder to distinguish the individuals to be counted.
For that matter, I’m not sure how easy it will be to acquire training sets for the recognizer software. This study had a very uniform nesting layout, so it was easy to get a representative subsample to train the algorithm. But if the nests are sited less uniformly, and mixed with other species and visual noise, it may be difficult to train the algorithm, at least without much larger samples.
Still, this technique is certainly a good idea when it can be made to work. UAVs are great “force multiplier” for ecologists, giving each scientist much greater range. Properly designed (by which I mean quiet) UAVs should be pretty unobtrusive, especially compared to human observers.
The same basic infrastructure can be used for many kinds of surface observations, not just bird colonies. It seems likely that UAV surveying will be a common scientific technique in the next few decades.
The image analysis also has the advantage that it can be repeated and improved. If the captured images are archived, then it will always be possible to go back with improved analytics and make new assessments from the samples. In fact, image archives are becoming an important part of the scientific record, and a tool for replication, cross validation, and data reuse.
- Jarrod C. Hodgson, Rowan Mott, Shane M. Baylis, Trung T. Pham, Simon Wotherspoon, Adam D. Kilpatrick, Ramesh Raja Segaran, Ian Reid, Aleks Terauds, and Lian Pin Koh, Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution:n/a-n/a, http://dx.doi.org/10.1111/2041-210X.12974
- University of Adelaide, #EpicDuckChallenge shows we can count on drones, in University of Adelaide – News. 2018. https://www.adelaide.edu.au/news/news98022.html